Data mining shape based data

ABSTRACT

Embodiments of the disclosure include a method for data mining shape based data, the method includes receiving shape data for each of a plurality of data entries and creating a first abstract from the shape data for each of the plurality of data entries. The method also includes organizing the first abstracts into a plurality of groups based on a criterion and creating a second abstract for each data entry in the plurality of groups based on the criterion and information derived from the first abstract.

BACKGROUND

The present invention relates to data mining, and more specifically, todata mining shape based data.

There are many data analysis applications where shapes based data mayprovide important information, such as data associated with designspecifications, layouts, plans, routes, traces or maps. Data analysiscan be enhanced by depicting data as shapes for the analysis orincluding layout information in the analysis, such as critical pathanalysis or diagnostics data analysis for yield learning. Currently, fordiagnostics data analysis, tests are performed on devices, such assemiconductor integrated circuit wafer tests, to detect possible errorsin the devices. Some of these tests are diagnosable and producediagnostic data that relate to possible errors in the devices.Diagnostic data are typically stored in a database and reviewed in anattempt to identify possible causes or similarities in the detectederrors. The diagnostic results may include large amounts of associatedshape based layout data. As a result of the large quantity of dataproduced, reviewing the diagnostic data to detect the presence ofsystematic errors or defects in the devices is a difficult and timeconsuming task.

SUMMARY

Embodiments include a method for data mining shape based data, themethod includes receiving shape data for each of a plurality of dataentries and creating a first abstract from the shape data for each ofthe plurality of data entries. The method also includes organizing thefirst abstracts into a plurality of groups based on a criterion andcreating a second abstract for each data entry in the plurality ofgroups based on the criterion and information derived from the firstabstract.

Embodiments include a computer system for data mining shape based data,the computer system including a data mining computer having a processor,the processor configured to perform a method. The method includesreceiving shape data for each of a plurality of data entries andcreating a first abstract from the shape data for each of the pluralityof data entries. The method also includes organizing the first abstractsinto a plurality of groups based on a criterion and creating a secondabstract for each data entry in the plurality of groups based on thecriterion and information derived from the first abstract.

Embodiments also include a computer program product for data miningshaped based data, the computer program product including a computerreadable storage medium having computer readable program code embodiedtherewith. The computer readable program code including computerreadable program code configured to perform a method. The methodincludes receiving shape data for each of a plurality of data entriesand creating a first abstract from the shape data for each of theplurality of data entries. The method also includes organizing the firstabstracts into a plurality of groups based on a criterion and creating asecond abstract for each data entry in the plurality of groups based onthe criterion and information derived from the first abstract.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 illustrates a block diagram of a computer system for use inpracticing the teachings herein;

FIG. 2 illustrates a flow diagram of a method for data mining shapebased data in accordance with an embodiment;

FIG. 3 illustrates a block diagram of a system for data mining shapedbased data in accordance with an embodiment; and

FIG. 4 illustrates a plurality of abstracts for multiple detected errorsin accordance with an embodiment.

DETAILED DESCRIPTION

In exemplary embodiments, a method for data mining shape based data isprovided. In one embodiment, diagnostic tests are performed on aplurality of semiconductor chips, or other devices, and errors ordefects detected during the diagnostic tests are saved as data entriesin a table or database. Each of the data entries includes shape dataassociated with the detected error or defect. In exemplary embodiments,a first abstract is created based on the shape data for each of the dataentries and the first abstracts are organized into groups of potentiallyrelated data entries based on a criterion. In exemplary embodiments, thecriterion may be one or more properties of the first abstracts. Inexemplary embodiments, the first abstract may be a visual representationof one or more characteristics of the shape data. For example, the firstabstract may be a graphical illustration of the location of the detectederror on the semiconductor chip. After groups of data entries areidentified, a second abstract is then created for each of the dataentries in the group based on the criterion. The second abstract ischosen based on information derived from the first abstract and the dataof the entries in the group. In exemplary embodiments, the secondabstract is a visual representation of the shape data that is differentthan the first abstract. For example, the second abstract may be agraphical representation with a different scale or orientation than thefirst abstract. In exemplary embodiments, a correlation between the dataentries in the group can be determined based on a comparison,classification, or categorization of the second abstracts.

FIG. 1 illustrates a block diagram of a computer system 100 for use inpracticing the teachings herein. The methods described herein can beimplemented in hardware, software (e.g., firmware), or a combinationthereof. In an exemplary embodiment, the methods described herein areimplemented in hardware, and may be part of the microprocessor of aspecial or general-purpose digital computer, such as a personalcomputer, workstation, minicomputer, or mainframe computer. The computersystem 100 therefore includes general-purpose computer 101.

In an exemplary embodiment, in terms of hardware architecture, as shownin FIG. 1, the computer 101 includes a processor 105, memory 110 coupledto a memory controller 115, and one or more input and/or output (I/O)devices 140, 145 (or peripherals) that are communicatively coupled via alocal input/output controller 135. The input/output controller 135 canbe, for example but not limited to, one or more buses or other wired orwireless connections, as is known in the art. The input/outputcontroller 135 may have additional elements, which are omitted forsimplicity, such as controllers, buffers (caches), drivers, repeaters,and receivers, to enable communications. Further, the local interfacemay include address, control, and/or data connections to enableappropriate communications among the aforementioned components.

The processor 105 is a hardware device for executing hardwareinstructions or software, particularly that stored in memory 110. Theprocessor 105 can be any custom made or commercially availableprocessor, a central processing unit (CPU), an auxiliary processor amongseveral processors associated with the computer 101, a semiconductorbased microprocessor (in the form of a microchip or chip set), amacroprocessor, or generally any device for executing instructions. Theprocessor 105 includes a cache 170, which may include, but is notlimited to, an instruction cache to speed up executable instructionfetch, a data cache to speed up data fetch and store, and a translationlookaside buffer (TLB) used to speed up virtual-to-physical addresstranslation for both executable instructions and data. The cache 170 maybe organized as a hierarchy of more cache levels (L1, L2, etc.).

The memory 110 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmableread only memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), programmable read only memory (PROM), tape, compactdisc read only memory (CD-ROM), disk, diskette, cartridge, cassette orthe like, etc.). Moreover, the memory 110 may incorporate electronic,magnetic, optical, and/or other types of storage media. Note that thememory 110 can have a distributed architecture, where various componentsare situated remote from one another, but can be accessed by theprocessor 105.

The instructions in memory 110 may include one or more separateprograms, each of which comprises an ordered listing of executableinstructions for implementing logical functions. In the example of FIG.1, the instructions in the memory 110 include a suitable operatingsystem (OS) 111. The operating system 111 essentially controls theexecution of other computer programs and provides scheduling,input-output control, file and data management, memory management, andcommunication control and related services.

In an exemplary embodiment, a conventional keyboard 150 and mouse 155can be coupled to the input/output controller 135. Other output devicessuch as the I/O devices 140, 145 may include input devices, for examplebut not limited to a printer, a scanner, microphone, and the like.Finally, the I/O devices 140, 145 may further include devices thatcommunicate both inputs and outputs, for instance but not limited to, anetwork interface card (NIC) or modulator/demodulator (for accessingother files, devices, systems, or a network), a radio frequency (RF) orother transceiver, a telephonic interface, a bridge, a router, and thelike. The system 100 can further include a display controller 125coupled to a display 130. In an exemplary embodiment, the system 100 canfurther include a network interface 160 for coupling to a network 165.The network 165 can be an IP-based network for communication between thecomputer 101 and any external server, client and the like via abroadband connection. The network 165 transmits and receives databetween the computer 101 and external systems. In an exemplaryembodiment, network 165 can be a managed IP network administered by aservice provider. The network 165 may be implemented in a wirelessfashion, e.g., using wireless protocols and technologies, such as WiFi,WiMax, etc. The network 165 can also be a packet-switched network suchas a local area network, wide area network, metropolitan area network,Internet network, or other similar type of network environment. Thenetwork 165 may be a fixed wireless network, a wireless local areanetwork (LAN), a wireless wide area network (WAN) a personal areanetwork (PAN), a virtual private network (VPN), intranet or othersuitable network system and includes equipment for receiving andtransmitting signals.

If the computer 101 is a PC, workstation, intelligent device or thelike, the instructions in the memory 110 may further include a basicinput output system (BIOS) (omitted for simplicity). The BIOS is a setof essential routines that initialize and test hardware at startup,start the OS 111, and support the transfer of data among the hardwaredevices. The BIOS is stored in ROM so that the BIOS can be executed whenthe computer 101 is activated. When the computer 101 is in operation,the processor 105 is configured to execute instructions stored withinthe memory 110, to communicate data to and from the memory 110, and togenerally control operations of the computer 101 pursuant to theinstructions.

Referring now to FIG. 2, a flow chart illustrating a method 200 for datamining shape based data in accordance with an embodiment is shown. Asshown at block 202, the method 200 includes receiving shape data foreach of a plurality of data entries. Next, as shown at block 204, themethod 200 includes creating a first abstract from the shape data foreach of the plurality of data entries. After the first abstracts arecreated, the first abstracts are organized into a plurality of groupsbased on a criterion, as shown at block 206. In exemplary embodiments,the criterion may include a wide range of properties of the firstabstracts that are selected to group abstracts that have similarproperties or traits into groups. For example, the criterion mayinclude, but is not limited to, a location of the detected error, anaspect ratio of the detected error, the general shape of the detectederror (i.e., horizontal or vertical bar), or the like. Next, as shown atblock 208, the method 200 includes creating a second abstract for eachdata entry in the plurality of groups based on the criterion. Forexample, if one or more of the first abstracts are placed into a groupbased on the location of the detected error, the second abstracts may becreated to show a more detailed view of the common location. Optionally,as shown at block 210, the method 200 may include determining acorrelation between each of the data entries in the group based on thesecond abstract.

In exemplary embodiments, the first and second abstracts are based on,and may illustrate, one or more characteristics of the shape data. Thecharacteristic of the shape data may include location information of thedetected defect, such as the location of the error on the chip.Characteristics of the shape data may also include information, such asthe shape or type of the detected defect. Characteristics of the shapedata may further include information about the device or test, such as alot, wafer, or chip identification, test type, test identification,error score, test date, and layer, circuit, net or pin information, orpower or performance or other variables that can be associated withshape data.

In exemplary embodiments, a group of multiple data entries may beidentified by comparing the first abstracts associated with each of thedata entries and grouping data entries that have one or morecharacteristics in common. For example, data entries that have detectederrors of a similar shape or in a similar location on a chip may beidentified as a group. In exemplary embodiments, the data entries may befiltered based on the one or more characteristics of the shape dataprior to identifying groups of data entries. For example, the dataentries may be filtered such that only data entries with error scoresabove a predetermined threshold are considered for grouping.

In exemplary embodiments, creating abstracts of the shape data andorganizing the shape data into groups based on criterion facilitate thecomparison of the shape data and simplify the process of identifyingpotential similarities in the shape data.

In exemplary embodiments, the shape data for the plurality of dataentries includes a large amount of data which makes the comparison ofthe shape data difficult. However, by abstracting the shape data andlooking for similarities in the abstracts, the comparison of the shapedata can be simplified. In exemplary embodiments, the abstraction andgrouping of the shape data based on criterion and information derivedfrom the abstracts are iterative processes that can be repeated multipletimes with various criterion and abstract definitions applied duringeach iteration.

In exemplary embodiments, the first abstract may be a graphicalrepresentation of the shape data that displays a first characteristic ofthe shape data and the second abstract may be a graphical representationof the shape data that displays a second characteristic of the shapedata. The abstracts can include graphical representations of the shapedata that utilize various shapes, colors, and other means forrepresenting the characteristic of the shape data. For example, theshape of the graphical representation may be indicative of the shape ofthe detected error, while the color may be used to indicate the severityof the detected error. In exemplary embodiments, the scale or size ofthe first abstract and second abstract may be different such thatvarious characteristics can be illustrated or emphasized. For example,the first abstract may have a first scale that is selected to adequatelyillustrate a location of a detected error on a chip and the secondabstract may have a scale that is an order of magnitude smaller toadequately illustrate the shape of the detected error.

In exemplary embodiments, a correlation between data entries in theidentified group can be determined based on a comparison of the secondabstracts. In exemplary embodiments, the second abstracts may be createdbased on a different characteristic than the characteristic used tocreate the first abstract. Furthermore, the characteristic used tocreate the second abstract is selected based on the criterion used togroup the first abstracts and information derived from the firstabstracts. For example, if the criterion used to group a plurality offirst abstracts is the location of a detected error then a group withlocations within a certain section of the layout may be further analyzedusing a second abstract definition based on the section of the layoutindicated by the first abstract.

In one embodiment, a user may be able to select the characteristic thatthe second abstract is created on and may be able to sort the secondabstracts based on the correlation. In exemplary embodiments, thecorrelation between the second abstracts can be used to detectsimilarities in the shape data that indicates underlying systematiccauses of the detected error. Likewise, the correlation between thesecond abstracts can be used to detect the absence of similarities inthe shape data that indicate that the detected errors may be unrelatedor random defects.

In exemplary embodiments, an abstract is created from the shape baseddata because the shape based data generated by the diagnostic process isoften complex and imprecise. Diagnostics results are error or defectcandidates. For example, the shape based data may contain shapes andlayers that are not actually defective, as well as the actual detecteddefect. In addition, a comparison of the entire shape based data, i.e.,non-abstracted data, will lead to only exact matches, while a comparisonof the abstracted shape data may enable a so called fuzzy comparison andreveal more subtle matches. Furthermore, by creating levels ofabstraction that hide details of the shape based data the quantificationof similarities is simplified, making automation of the comparison ofthe abstractions possible.

In exemplary embodiments, abstractions may be used for multiple purposesincluding, but not limited to, autonomous data mining, user guided datamining (semi-automatic processing), and data presentation foruser-machine interactions. In one embodiment, multiple abstractions maybe applied to the shape based data including, but not limited to, layer,wiring length, number of vias, number of non-redundant vias, number ofconnected components, highest layer, bounding box of all shapes,bounding box for each layer, bounding box for each polygon, number oftransistors, size of driving transistor, ratio of capacitances to VDDand GND, shape pattern, combinations of shapes, structures, imagefeatures, angles, any comparable property. In exemplary embodiments, theabstracts may be organized into groups based a quantification ofsimilarity that can use linear or logarithmic intervals.

Referring now to FIG. 3, a block diagram of a system 300 for data miningshape based data in accordance with an embodiment is shown. The system300 includes a data mining computer 304, which may be similar to thecomputer shown in described with reference to FIG. 1. The data miningcomputer 304 is configured to receive analysis input data, such asdiagnostics results from test and diagnosis simulation, device logisticsdata, and shapes data. In addition, the system 300 includes a datastorage device 310, which is configured to store the analysis input data302. In exemplary embodiments, the data storage device 310 may be partof the data mining computer 304 or the data storage device 310 may beembodied in a separate device. The data mining computer 304 is alsoconfigured to receive user input 306. In exemplary embodiments, the userinput 306 may be used by the data mining computer 304 to select thecharacteristics used in creating the first and/or second abstracts, insorting the abstracts, in grouping the abstracts, or the like. The datamining computer 304 is further configured to display the abstracts on adisplay device 308, which may be part of the data mining computer 304 ormay be embodied in a separate device.

Referring now to FIG. 4, a plurality of abstracts illustrating shapedata for multiple detected errors in accordance with an embodiment isshown. As illustrated four exemplary defects are shown and each defectis represented by four abstracts. Abstracts 400 a, 400 b, 400 c and 400d all illustrate a first defect in a different scale; Abstracts 402 a,402 b, 402 c and 402 d all illustrate a second defect in a differentscale; Abstracts 404 a, 404 b, 404 c and 404 d all illustrate a thirddefect in a different scale; and Abstracts 406 a, 406 b, 406 c and 406 dall illustrate a fourth defect in a different scale. As illustrated,various abstracts can be utilized to illustrate differentcharacteristics of shape data corresponding to a detected error. Forexample, abstracts 400 d, 402 d, 404 d and 406 d may be used toillustrate a location of a detected error while abstracts 400 b, 402 b,404 b and 406 b may be used to illustrate a shape of the detected error.In exemplary embodiments, the abstracts created and used by the datamining system may have different scales, different orientations, or maybe based on different characteristics of the shape data for the detectederrors.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of onemore other features, integers, steps, operations, element components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There may bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of the invention. Forinstance, the steps may be performed in a differing order or steps maybe added, deleted or modified. All of these variations are considered apart of the claimed invention.

While the preferred embodiment to the invention had been described, itwill be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow. These claims should be construedto maintain the proper protection for the invention first described.

What is claimed is:
 1. A computer system for data mining shape baseddata, the computer system comprising: a data mining computer having aprocessor, the processor configured to perform a method comprising:receiving a shape data for each of a plurality of data entries; creatinga first abstract from the shape data for each of the plurality of dataentries, wherein the first abstract is a graphic illustration of alocation of a detected error on a semiconductor chip; organizing thefirst abstracts into a plurality of groups based on a first criterion,wherein the first criterion is the location of the detected error;creating a second abstract for each data entry in the plurality ofgroups based on a second criterion, wherein the second criterion is theshape of the detected error, and information derived from the firstabstract, wherein the second abstract is a visual representation of thedetected error on the semiconductor chip that illustrates a moredetailed view of a common location of detected errors; and determining acorrelation between each of the data entries in the group based on thesecond abstract, wherein similarities in the shapes of the detectederrors in the common location indicate that the detected errors are notrandom defects.
 2. The computer system of claim 1, wherein the firstabstract includes a characteristic of the shape data.
 3. The computersystem of claim 2, wherein each of the plurality of groups of dataentries have at least one common characteristic.
 4. The computer systemof claim 1, wherein the second abstract includes a second characteristicof the shape data and the correlation is based on a similarity of thesecond characteristic.
 5. The computer system of claim 1, furthercomprises displaying a graphical representation of the second abstracts.6. The computer system of claim 5, further comprising: sorting thegraphical representation of the second abstracts based on thecorrelation.
 7. A computer program product for data mining shape baseddata, the computer program product comprising a computer readablestorage medium having computer readable program code embodied therewith,the computer readable program code executable by a computer to perform amethod comprising: receiving a shape data for each of a plurality ofdata entries; creating a first abstract from the shape data for each ofthe plurality of data entries, wherein the first abstract is a graphicillustration of a location of a detected error on a semiconductor chip;organizing the first abstracts into a plurality of groups based on afires criterion, wherein the first criterion is a location of thedetected error; creating a second abstract for each data entry in theplurality of groups based on a second criterion, wherein the secondcriterion is the shape of the detected error, and information derivedfrom the first abstract, wherein the second abstract is a visualrepresentation of the detected error on the semiconductor chip thatillustrates a more detailed view of a common location of detectederrors; and determining a correlation between each of the data entriesin the group based on the second abstract, wherein similarities in theshapes of the detected errors in the common location indicate that thedetected errors are not random defects.
 8. The computer program productof claim 7, wherein the first abstract includes a characteristic of theshape data.
 9. The computer program product of claim 8, wherein each ofthe plurality of groups of data entries have at least one commoncharacteristic.
 10. The computer program product of claim 7, wherein thesecond abstract includes a second characteristic of the shape data andthe correlation is based on a similarity of the second characteristic.11. The computer program product of claim 7, further comprisesdisplaying a graphical representation of the second abstracts.
 12. Thecomputer program product of claim 11, further comprising: sorting thegraphical representation of the second abstracts based on thecorrelation.